set -x
ENGINE=${1:-vllm}

source /usr/local/Ascend/ascend-toolkit/set_env.sh
source /usr/local/Ascend/nnal/atb/set_env.sh

# Some models are optimized by vllm ascend. While in some case, e.g. rlhf training, 
# the optimized model may not be suitable. In this case, set this value to 0 to disable the optimized model.
export USE_OPTIMIZED_MODEL=0
export VLLM_USE_V1=1
export HYDRA_FULL_ERROR=1

SAVE_DIR="/home/ma-user/work/verl_ckpts"
output_dir=./train_output/qwen2_5_vl_7b/
BASEDIR="/home/ma-user/work/"
task_name="qwen2_5_vl_7b_function_rm_grpo_npu_$(date +%Y%m%d_%H%M%S)"
exp_name="test"
project_name="grpo_qwen2_5_vl_7b_function_rm_npu"
mkdir -p ${BASEDIR}/logs/${exp_name}
python3 -u -m verl.trainer.main_ppo \
    algorithm.adv_estimator=grpo \
    data.train_files=/home/ma-user/work/datasets/Geometry3K/train.parquet \
    data.val_files=/home/ma-user/work/datasets/Geometry3K/test.parquet \
    data.train_batch_size=64 \
    data.max_prompt_length=4096 \
    data.max_response_length=8192 \
    data.filter_overlong_prompts=True \
    data.truncation='error' \
    data.image_key=images \
    actor_rollout_ref.model.path=/home/ma-user/work/models/Qwen2.5-VL-7B-Instruct \
    actor_rollout_ref.actor.optim.lr=1e-6 \
    actor_rollout_ref.model.use_remove_padding=True \
    actor_rollout_ref.actor.ppo_mini_batch_size=64 \
    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \
    actor_rollout_ref.actor.use_kl_loss=True \
    actor_rollout_ref.actor.kl_loss_coef=0.01 \
    actor_rollout_ref.actor.kl_loss_type=low_var_kl \
    actor_rollout_ref.actor.entropy_coeff=0 \
    actor_rollout_ref.actor.use_torch_compile=False \
    actor_rollout_ref.model.enable_gradient_checkpointing=True \
    actor_rollout_ref.actor.fsdp_config.param_offload=true \
    actor_rollout_ref.actor.fsdp_config.optimizer_offload=true \
    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=2 \
    actor_rollout_ref.rollout.tensor_model_parallel_size=2 \
    actor_rollout_ref.rollout.name=$ENGINE \
    actor_rollout_ref.rollout.engine_kwargs.vllm.disable_mm_preprocessor_cache=True \
    actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \
    actor_rollout_ref.rollout.enable_chunked_prefill=False \
    actor_rollout_ref.rollout.enforce_eager=false \
    actor_rollout_ref.rollout.max_num_batched_tokens=32768 \
    actor_rollout_ref.rollout.free_cache_engine=True \
    actor_rollout_ref.rollout.n=4 \
    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \
    actor_rollout_ref.ref.fsdp_config.param_offload=True \
    algorithm.use_kl_in_reward=False \
    trainer.critic_warmup=0 \
    trainer.rollout_data_dir=${output_dir}/${task_name}\
    trainer.logger=console \
    trainer.project_name=${project_name} \
    trainer.experiment_name='qwen2_5_vl_7b_function_rm' \
    trainer.n_gpus_per_node=16 \
    trainer.nnodes=1 \
    trainer.default_local_dir=${SAVE_DIR}/${project_name}/${exp_name}/${task_name} \
    trainer.save_freq=3 \
    trainer.test_freq=2 \
    trainer.total_epochs=15 \
    trainer.device=npu "$@" \
    trainer.val_before_train=False \
    2>&1 | tee /home/ma-user/work/logs/${exp_name}/${task_name}.log